Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Decentralized Schemes for Size Estimation in Large and Dynamic Groups
NCA '05 Proceedings of the Fourth IEEE International Symposium on Network Computing and Applications
Distributed collaboration for event detection in wireless sensor networks
MPAC '05 Proceedings of the 3rd international workshop on Middleware for pervasive and ad-hoc computing
Peer counting and sampling in overlay networks: random walk methods
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
CollECT: Collaborative Event deteCtion and Tracking in Wireless Heterogeneous Sensor Networks
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
LATEX DSL: A Coverage Control Protocol for heterogeneous Wireless Sensor Networks
ICSNC '07 Proceedings of the Second International Conference on Systems and Networks Communications
Plan-based complex event detection across distributed sources
Proceedings of the VLDB Endowment
Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications
Proceedings of the 6th ACM conference on Embedded network sensor systems
ACM Transactions on Computer Systems (TOCS)
Monitoring heritage buildings with wireless sensor networks: The Torre Aquila deployment
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
A Location-Independent Node Scheduling for Heterogeneous Wireless Sensor Networks
SENSORCOMM '09 Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications
Redeployment based sensing hole mitigation in wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Gossip-Based Self-Management of a Recursive Area Hierarchy for Large Wireless SensorNets
IEEE Transactions on Parallel and Distributed Systems
U-connect: a low-latency energy-efficient asynchronous neighbor discovery protocol
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Data filtering and dynamic sensing for continuous monitoring in wireless sensor networks
International Journal of Autonomous and Adaptive Communications Systems
Density-varying high-end sensor placement in heterogeneous wireless sensor networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Identifying frequent items in a network using gossip
Journal of Parallel and Distributed Computing
ASH: Tackling Node Mobility in Large-Scale Networks
SASO '10 Proceedings of the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
ChurnDetect: a gossip-based churn estimator for large-scale dynamic networks
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
NetDetect: Neighborhood Discovery in Wireless Networks Using Adaptive Beacons
SASO '11 Proceedings of the 2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems
Middleware to support sensor network applications
IEEE Network: The Magazine of Global Internetworking
| Hi-index | 0.00 |
The density estimation of diverse sensor types in a heterogeneous sensor network is an important service that can be used in clustering schemes, node redeployment and sleep scheduling strategies. Similar to any wireless sensor network service, energy efficiency is one of the main requirements. The service has to provide an updated estimation at each node. Network dynamics, especially node mobility, introduce new challenges. Moreover, churn makes the problem even more complicated. In this paper we introduce a new approach called Gossip based Density Estimation GDE for heterogeneous dynamic networks. The devised method is able to cope with node mobility and churn, as well as redeployment of new nodes. It is fully distributed and adaptive to network dynamics. We analyse the effect of mobility as well as increased scale in the number of clusters and the quantity of nodes. The simulation results support the idea that our algorithm has a fast convergence speed and provides more accurate estimation compared to similar approaches.